update examples

README.md

@@ -18,7 +18,11 @@ python3 -m venv .venv
 # activate the virtual env
 . .venv/bin/activate
 
-pip install langroid
+# install with `hf-embeddings` extra to be able to use sentence_transformers embeddings
+pip install "langroid[hf-embeddings]"
+
+# or to update an existing installation:
+pip install --upgrade "langroid[hf-embeddings]"
 ```
 
 

examples/basic/chat-search.py

@@ -1,11 +1,25 @@
+"""
+This is a basic example of a chatbot that uses the GoogleSearchTool:
+when the LLM doesn't know the answer to a question, it will use the tool to
+search the web for relevant results, and then use the results to answer the
+question.
+
+NOTE: running this example requires setting the GOOGLE_API_KEY and GOOGLE_CSE_ID
+environment variables in your `.env` file, as explained in the
+[README](https://github.com/langroid/langroid#gear-installation-and-setup).
+"""
+
 import typer
 from rich import print
 from rich.prompt import Prompt
+from pydantic import BaseSettings
+from dotenv import load_dotenv
 
 from langroid.agent.chat_agent import ChatAgent, ChatAgentConfig
 from langroid.agent.task import Task
-from langroid.agent.stateless_tools.google_search_tool import GoogleSearchTool
-from langroid.language_models.openai_gpt import OpenAIChatModel, OpenAIGPTConfig
+from langroid.language_models.base import LocalModelConfig
+from langroid.agent.tools.google_search_tool import GoogleSearchTool
+from langroid.language_models.openai_gpt import OpenAIGPTConfig
 from langroid.utils.configuration import set_global, Settings
 from langroid.utils.logging import setup_colored_logging
 
@@ -15,10 +29,27 @@
 setup_colored_logging()
 
 
-def chat() -> None:
+class CLIOptions(BaseSettings):
+    local: bool = False
+    api_base: str = "http://localhost:8000/v1"
+    local_model: str = ""
+    local_ctx: int = 2048
+    # use completion endpoint for chat?
+    # if so, we should format chat->prompt ourselves, if we know the required syntax
+    completion: bool = False
+
+    class Config:
+        extra = "forbid"
+        env_prefix = ""
+
+
+def chat(opts: CLIOptions) -> None:
     print(
         """
-        [blue]Welcome to the basic chatbot!
+        [blue]Welcome to the Google Search chatbot!
+        I will try to answer your questions, relying on (summaries of links from) 
+        Google Search when needed.
+        
         Enter x or q to quit at any point.
         """
     )
@@ -27,24 +58,60 @@ def chat() -> None:
         default="Default: 'You are a helpful assistant'",
     )
 
+    load_dotenv()
+
+    # create the appropriate OpenAIGPTConfig depending on local model or not
+
+    if opts.local or opts.local_model:
+        # assumes local endpoint is either the default http://localhost:8000/v1
+        # or if not, it has been set in the .env file as the value of
+        # OPENAI_LOCAL.API_BASE
+        local_model_config = LocalModelConfig(
+            api_base=opts.api_base,
+            model=opts.local_model,
+            context_length=opts.local_ctx,
+            use_completion_for_chat=opts.completion,
+        )
+        llm_config = OpenAIGPTConfig(
+            local=local_model_config,
+            timeout=180,
+        )
+    else:
+        # defaults to chat_model = OpenAIChatModel.GPT4
+        llm_config = OpenAIGPTConfig()
+
     config = ChatAgentConfig(
         system_message=sys_msg,
-        llm=OpenAIGPTConfig(
-            chat_model=OpenAIChatModel.GPT4,
-        ),
+        llm=llm_config,
+        vecdb=None,
     )
     agent = ChatAgent(config)
     agent.enable_message(GoogleSearchTool)
     task = Task(
         agent,
         system_message="""
         You are a helpful assistant. You will try your best to answer my questions.
-        If you don't know you can use up to 2 results from the `web search` 
-        tool/function-call to help you with answering the question.
+        If you cannot answer from your own knowledge, you can use up to 5 
+        results from the `web_search` tool/function-call to help you with 
+        answering the question.
         Be very concise in your responses, use no more than 1-2 sentences.
+        When you answer based on a web search, First show me your answer, 
+        and then show me the SOURCE(s) and EXTRACT(s) to justify your answer,
+        in this format:
+        
+        <your answer here>
+        SOURCE: https://www.wikihow.com/Be-a-Good-Assistant-Manager
+        EXTRACT: Be a Good Assistant ... requires good leadership skills.
+        
+        SOURCE: ...
+        EXTRACT: ...
+        
+        For the EXTRACT, ONLY show up to first 3 words, and last 3 words.
         """,
     )
-    task.run()
+    # local models do not like the first message to be empty
+    user_message = "Hello." if (opts.local or opts.local_model) else None
+    task.run(user_message)
 
 
 @app.command()
@@ -55,6 +122,19 @@ def main(
     cache_type: str = typer.Option(
         "redis", "--cachetype", "-ct", help="redis or momento"
     ),
+    local: bool = typer.Option(False, "--local", "-l", help="use local llm"),
+    local_model: str = typer.Option(
+        "", "--local_model", "-lm", help="local model path"
+    ),
+    api_base: str = typer.Option(
+        "http://localhost:8000/v1", "--api_base", "-api", help="local model api base"
+    ),
+    local_ctx: int = typer.Option(
+        2048, "--local_ctx", "-lc", help="local llm context size (default 2048)"
+    ),
+    completion: bool = typer.Option(
+        False, "--completion", "-c", help="use completion endpoint for chat"
+    ),
 ) -> None:
     set_global(
         Settings(
@@ -64,7 +144,14 @@ def main(
             cache_type=cache_type,
         )
     )
-    chat()
+    opts = CLIOptions(
+        local=local,
+        api_base=api_base,
+        local_model=local_model,
+        local_ctx=local_ctx,
+        completion=completion,
+    )
+    chat(opts)
 
 
 if __name__ == "__main__":

examples/data-qa/sql-chat/demo.json

@@ -0,0 +1,25 @@
+{
+    "departments": {
+        "description": "The 'departments' table holds details about the various departments. It relates to the 'employees' table via a foreign key in the 'employees' table.",
+        "columns": {
+            "id": "A unique identifier for a department. This ID is used as a foreign key in the 'employees' table.",
+            "name": "The name of the department."
+        }
+    },
+    "employees": {
+        "description": "The 'employees' table contains information about the employees. It relates to the 'departments' and 'sales' tables via foreign keys.",
+        "columns": {
+            "id": "A unique identifier for an employee. This ID is used as a foreign key in the 'sales' table.",
+            "name": "The name of the employee.",
+            "department_id": "The ID of the department the employee belongs to. This is a foreign key referencing the 'id' in the 'departments' table."
+        }
+    },
+    "sales": {
+        "description": "The 'sales' table keeps a record of all sales made by employees. It relates to the 'employees' table via a foreign key.",
+        "columns": {
+            "id": "A unique identifier for a sale.",
+            "amount": "The amount of the sale in eastern Caribbean dollars (XCD).",
+            "employee_id": "The ID of the employee who made the sale. This is a foreign key referencing the 'id' in the 'employees' table."
+        }
+    }
+}